The goal of this project is to develop improved statistical methods for toxicology studies. Work has proceeded in two areas: (1) the analysis of tumor multiplicity data, and (2) risk assessment and testing in toxicology studies that measure multiple endpoints. In tumorigenicity experiments that utilize animal models of skin and breast cancer, the tumor burden on each animal is evident (palpable) and can be measured at repeated observation times. These types of studies are widely used to explore cancer mechanisms, and to identify carcinogens and compounds with potential chemopreventive attributes. We have developed methods of (1) assessing treatment effects on tumor burden in the absence of data on the individual tumor appearance times; and (2) distinguishing treatment effects on the number of initiated cells and the tumor growth rate. Multiple endpoints are often measured in reproductive and developmental toxicity studies. We have developed methods for assessing overall toxic effects in reproductive experiments when data include both the number of subunits per dam (litter size, number of implants) and multiple binary outcomes on each subunit (low birth weight, malformation). We have also developed a general framework for modeling of multivariate clustered data that enables joint estimation of effects on disparate outcomes such as the number of implantation sites per animal, the proportion of dead fetuses per dam, the proportion of malformed fetuses per dam, and birth weight. Methods are under development that are robust to distributional assumptions.